Topsis-Aryan-102316004 1.0.0

A Python package for TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)

Topsis-Aryan-102316004

Project on PyPI: https://pypi.org/project/Topsis-Aryan-102316004/

A Python package to implement TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), a multi-criteria decision analysis method.

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📋 Table of Contents

• Installation
• Part 1: Command Line Usage
• Part 2: Library Usage
• Part 3: Web Service
• License

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📦 Installation

You can install the package directly from PyPI:

pip install Topsis-Aryan-102316004

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Part 1: Command Line Usage

You can use the topsis command directly in your terminal to process CSV files.

Syntax

topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>

Parameters

1. InputDataFile: Path to the input CSV file. Must contain numeric data from the 2nd column onwards.
2. Weights: Comma-separated weights (e.g., 1,1,1,1).
3. Impacts: Comma-separated impacts (+ for beneficial, - for non-beneficial).
4. OutputResultFileName: Name of the output CSV file to save results.

Example

topsis data.csv "1,1,1,1,1" "+,+,+,+,+" result.csv

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Part 2: Library Usage

You can import the package in your Python scripts.

from topsis.main import topsis_logic
import pandas as pd

# Load your dataset
df = pd.read_csv("data.csv")

# processing...
# ...

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Part 3: Web Service

A user-friendly web interface is provided to use the TOPSIS method without writing code.

Features

• Upload CSV: Easily upload your input file.
• Custom Parameters: Enter weights and impacts.
• Email Results: Get the resultant CSV file directly in your inbox.
• Interactive Table: View the results (Topsis Score and Rank) instantly.

Running the Web App

Ensure you have streamlit installed (pip install streamlit).

streamlit run app.py

Screenshot

Note: Provide your sender Gmail and App Password in the sidebar to enable email functionality.

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� Sample Data

Input Data (data.csv)

Fund Name	P1	P2	P3	P4	P5
M1	0.67	0.45	6.5	42.6	12.56
M2	0.6	0.36	3.6	53.3	14.47
M3	0.82	0.67	3.8	63.1	17.1
M4	0.6	0.36	3.5	69.2	18.42
M5	0.76	0.58	4.8	43	12.29
M6	0.69	0.48	6.6	48.7	14.12
M7	0.79	0.62	4.8	59.2	16.35
M8	0.84	0.71	6.5	34.5	10.64

Output Result (result.csv)

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.67	0.45	6.5	42.6	12.56	0.4354	6
M2	0.6	0.36	3.6	53.3	14.47	0.3038	8
M3	0.82	0.67	3.8	63.1	17.1	0.6269	2
M4	0.6	0.36	3.5	69.2	18.42	0.4730	5
M5	0.76	0.58	4.8	43.0	12.29	0.4177	7
M6	0.69	0.48	6.6	48.7	14.12	0.5234	4
M7	0.79	0.62	4.8	59.2	16.35	0.6514	1
M8	0.84	0.71	6.5	34.5	10.64	0.5237	3

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�📄 License

This project is licensed under the MIT License.